To investigate the robustness of scales, we validate several kinds of testing scales like those in [1]}.As shown in Figure REF , it shows that different testing scales can result in obviously different performances. Even through the short size \(s\)  of the test image is consistent with the size (\(640\times 640\) ) of the training image, it also can not ensure achieving the optimal performance.Under many conditions, it achieves the best performance, when using a smaller size (, 512). The reason may be ascribed to the domain shift of the scale between the training data and the testing data. In effect, existing methods usually utilize the testing scale with no evidence. Sometimes, when we only change the testing scale, it would also bring an obvious performance improvement. In the past few years, learning a scale-robust detector for the arbitrary-shape scene texts with diversified scales and aspect ratios nearly draws little attention in the field of scene text detection.
<FIGURE>